Add OpenVino Detector (#3768)

* Initial work for adding OpenVino detector.  Not functional

* Load model and submit for inference.

Sucessfully load model and initialize OpenVino engine with either CPU or GPU as device.
Does not parse results for objects.

* Detection working with ssdlite_mobilenetv2 FP16 model

* Add OpenVIno support and model to docker image

* Add documentation for OpenVino detector configuration

* Adds support for ARM32/ARM64 and the Myriad X hardware

-  Use custom-built openvino wheel for all platforms
-  Add libusb build without udev for NCS2 support

* Add documentation around Intel CPU requirements and NCS2 setup

* Print all available output tensors

* Update documentation for config parameters

frigate/detectors/openvino.py

@@ -0,0 +1,54 @@
+import logging
+import numpy as np
+import openvino.runtime as ov
+
+from frigate.detectors.detection_api import DetectionApi
+
+
+logger = logging.getLogger(__name__)
+
+
+class OvDetector(DetectionApi):
+    def __init__(self, det_device=None, model_config=None, num_threads=1):
+        self.ov_core = ov.Core()
+        self.ov_model = self.ov_core.read_model(model_config.path)
+
+        self.interpreter = self.ov_core.compile_model(
+            model=self.ov_model, device_name=det_device
+        )
+        logger.info(f"Model Input Shape: {self.interpreter.input(0).shape}")
+        self.output_indexes = 0
+        while True:
+            try:
+                tensor_shape = self.interpreter.output(self.output_indexes).shape
+                logger.info(f"Model Output-{self.output_indexes} Shape: {tensor_shape}")
+                self.output_indexes += 1
+            except:
+                logger.info(f"Model has {self.output_indexes} Output Tensors")
+                break
+
+    def detect_raw(self, tensor_input):
+
+        infer_request = self.interpreter.create_infer_request()
+        infer_request.infer([tensor_input])
+
+        results = infer_request.get_output_tensor()
+
+        detections = np.zeros((20, 6), np.float32)
+        i = 0
+        for object_detected in results.data[0, 0, :]:
+            if object_detected[0] != -1:
+                logger.debug(object_detected)
+            if object_detected[2] < 0.1 or i == 20:
+                break
+            detections[i] = [
+                object_detected[1],  # Label ID
+                float(object_detected[2]),  # Confidence
+                object_detected[4],  # y_min
+                object_detected[3],  # x_min
+                object_detected[6],  # y_max
+                object_detected[5],  # x_max
+            ]
+            i += 1
+
+        return detections